Memory Performance

Specific source memory increased for high reward memoranda after 24 hrs across all ages

  1. Linear age model is best-fitting
  2. Main effects of reward level (p < 0.001) and retrieval condition (p < 0.001)
## Data: assochitd2.long
## Models:
## assocmemmodelagelin: mem ~ RewardType * RetCond * ageScaled + HighRewardStim + (1 | subid)
## assocmemmodelagesq: mem ~ RewardType * RetCond * ageScaled + RewardType * RetCond * ageScaledsq + HighRewardStim + (1 | subid)
##                     npar     AIC     BIC logLik deviance  Chisq Df Pr(>Chisq)
## assocmemmodelagelin   11 -570.13 -527.51 296.07  -592.13                     
## assocmemmodelagesq    15 -564.11 -505.99 297.06  -594.11 1.9795  4     0.7395
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: mem
##                                 Chisq Df Pr(>Chisq)    
## (Intercept)                  825.5357  1  < 2.2e-16 ***
## RewardType                    17.8924  1  2.338e-05 ***
## RetCond                       49.5615  1  1.922e-12 ***
## ageScaled                      1.9936  1     0.1580    
## HighRewardStim                 0.6316  1     0.4268    
## RewardType:RetCond             1.8486  1     0.1739    
## RewardType:ageScaled           0.0000  1     0.9995    
## RetCond:ageScaled              0.1990  1     0.6555    
## RewardType:RetCond:ageScaled   0.7667  1     0.3812    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

VTC Pattern Similarity

ERS

  1. Quadratic age model is best-fitting
  2. Main effect of age (p < 0.01), regardless of controlling for voxel number or removing 1 run subs
## Data: VTCers.long
## Models:
## VTCersd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## VTCersd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
##                     npar     AIC     BIC logLik deviance  Chisq Df Pr(>Chisq)  
## VTCersd1modelagelin    7 -635.77 -613.50 324.88  -649.77                       
## VTCersd1modelagesq     9 -639.19 -610.56 328.60  -657.19 7.4258  2    0.02441 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                           Chisq Df Pr(>Chisq)    
## (Intercept)            160.5749  1  < 2.2e-16 ***
## RewardType               1.5529  1   0.212712    
## ageScaled                0.9232  1   0.336647    
## ageScaledsq              7.0190  1   0.008065 ** 
## HighRewardStim           0.3076  1   0.579187    
## RewardType:ageScaled     0.0256  1   0.872976    
## RewardType:ageScaledsq   0.3526  1   0.552618    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                           Chisq Df Pr(>Chisq)    
## (Intercept)            160.1296  1  < 2.2e-16 ***
## RewardType               1.5529  1   0.212712    
## ageScaled                0.8688  1   0.351278    
## ageScaledsq              6.9375  1   0.008441 ** 
## HighRewardStim           0.3129  1   0.575930    
## goldencvoxnumScaled      0.6727  1   0.412125    
## RewardType:ageScaled     0.0256  1   0.872976    
## RewardType:ageScaledsq   0.3526  1   0.552618    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                           Chisq Df Pr(>Chisq)    
## (Intercept)            134.2317  1    < 2e-16 ***
## RewardType               0.8491  1    0.35679    
## ageScaled                0.4723  1    0.49193    
## ageScaledsq              3.9887  1    0.04581 *  
## HighRewardStim           1.0225  1    0.31192    
## RewardType:ageScaled     0.1597  1    0.68940    
## RewardType:ageScaledsq   0.4044  1    0.52482    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Encoding Similarity

  1. Linear age model is best-fitting
  2. Main effects of reward level (p < 0.001) and age (p < 0.05), regardless of controlling for voxel number or removing 1 run subs
## Data: VTCencsim.long
## Models:
## VTCencsimd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## VTCencsimd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
##                        npar     AIC     BIC logLik deviance  Chisq Df
## VTCencsimd1modelagelin    7 -434.15 -413.27 224.08  -448.15          
## VTCencsimd1modelagesq     9 -431.66 -404.81 224.83  -449.66 1.5103  2
##                        Pr(>Chisq)
## VTCencsimd1modelagelin           
## VTCencsimd1modelagesq      0.4699
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                         Chisq Df Pr(>Chisq)    
## (Intercept)          257.9367  1  < 2.2e-16 ***
## RewardType            13.8480  1  0.0001982 ***
## ageScaled              4.3820  1  0.0363212 *  
## HighRewardStim         1.3402  1  0.2469974    
## RewardType:ageScaled   0.1209  1  0.7280601    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                         Chisq Df Pr(>Chisq)    
## (Intercept)          257.4219  1  < 2.2e-16 ***
## RewardType            13.8480  1  0.0001982 ***
## ageScaled              3.8598  1  0.0494559 *  
## HighRewardStim         1.3290  1  0.2489831    
## goldencvoxnumScaled    0.7466  1  0.3875695    
## RewardType:ageScaled   0.1209  1  0.7280601    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                         Chisq Df Pr(>Chisq)    
## (Intercept)          257.9367  1  < 2.2e-16 ***
## RewardType            13.8480  1  0.0001982 ***
## ageScaled              4.3820  1  0.0363212 *  
## HighRewardStim         1.3402  1  0.2469974    
## RewardType:ageScaled   0.1209  1  0.7280601    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Retrieval Similarity

  1. Linear age model is best-fitting
  2. No effects
## Data: VTCretsim.long
## Models:
## VTCretsimd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## VTCretsimd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
##                        npar     AIC     BIC logLik deviance  Chisq Df
## VTCretsimd1modelagelin    7 -530.23 -509.34 272.11  -544.23          
## VTCretsimd1modelagesq     9 -530.35 -503.50 274.17  -548.35 4.1215  2
##                        Pr(>Chisq)
## VTCretsimd1modelagelin           
## VTCretsimd1modelagesq      0.1274
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                         Chisq Df Pr(>Chisq)    
## (Intercept)          378.3351  1     <2e-16 ***
## RewardType             0.9268  1     0.3357    
## ageScaled              1.4320  1     0.2314    
## HighRewardStim         1.4449  1     0.2293    
## RewardType:ageScaled   0.0286  1     0.8656    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Anterior Hippocampus Pattern Similarity

ERS

  1. Linear age model is best-fitting
  2. Interaction between reward level and age (p < 0.01), regardless of controlling for voxel number or removing 1 run subs
## Data: anthippers.long
## Models:
## anthippersd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## anthippersd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
##                         npar     AIC     BIC logLik deviance  Chisq Df
## anthippersd1modelagelin    7 -764.18 -741.91 389.09  -778.18          
## anthippersd1modelagesq     9 -763.92 -735.28 390.96  -781.92 3.7401  2
##                         Pr(>Chisq)
## anthippersd1modelagelin           
## anthippersd1modelagesq      0.1541
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                       Chisq Df Pr(>Chisq)   
## (Intercept)          2.4476  1   0.117707   
## RewardType           1.5299  1   0.216122   
## ageScaled            1.5259  1   0.216727   
## HighRewardStim       0.0029  1   0.956885   
## RewardType:ageScaled 9.4089  1   0.002159 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                       Chisq Df Pr(>Chisq)   
## (Intercept)          2.4261  1   0.119327   
## RewardType           1.5299  1   0.216122   
## ageScaled            1.5405  1   0.214549   
## HighRewardStim       0.0033  1   0.954171   
## goldencvoxnumScaled  0.2511  1   0.616322   
## RewardType:ageScaled 9.4089  1   0.002159 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                       Chisq Df Pr(>Chisq)  
## (Intercept)          1.0369  1     0.3086  
## RewardType           1.1451  1     0.2846  
## ageScaled            0.1238  1     0.7250  
## HighRewardStim       0.0297  1     0.8632  
## RewardType:ageScaled 4.9365  1     0.0263 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Encoding Similarity

  1. Linear age model is best-fitting
  2. No effects, regardless of controlling for voxel number
## Data: anthippencsim.long
## Models:
## anthippencsimd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## anthippencsimd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
##                            npar     AIC     BIC logLik deviance  Chisq Df
## anthippencsimd1modelagelin    7 -731.95 -711.07 372.98  -745.95          
## anthippencsimd1modelagesq     9 -729.11 -702.26 373.55  -747.11 1.1562  2
##                            Pr(>Chisq)
## anthippencsimd1modelagelin           
## anthippencsimd1modelagesq       0.561
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                        Chisq Df Pr(>Chisq)    
## (Intercept)          26.1044  1  3.234e-07 ***
## RewardType            0.3252  1     0.5685    
## ageScaled             0.2013  1     0.6537    
## HighRewardStim        0.9699  1     0.3247    
## RewardType:ageScaled  1.0964  1     0.2951    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                        Chisq Df Pr(>Chisq)    
## (Intercept)          27.5386  1   1.54e-07 ***
## RewardType            0.3252  1     0.5685    
## ageScaled             0.4009  1     0.5266    
## HighRewardStim        0.9004  1     0.3427    
## goldencvoxnumScaled   1.6902  1     0.1936    
## RewardType:ageScaled  1.0964  1     0.2951    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Retrieval Similarity

  1. Linear age model is best-fitting
  2. No effects, regardless of controlling for voxel number
## Data: anthippretsim.long
## Models:
## anthippretsimd1modelagelin: similarity ~ RewardType * ageScaled + HighRewardStim + (1 | subid)
## anthippretsimd1modelagesq: similarity ~ RewardType * ageScaled + RewardType * ageScaledsq + HighRewardStim + (1 | subid)
##                            npar     AIC     BIC logLik deviance  Chisq Df
## anthippretsimd1modelagelin    7 -690.46 -669.57 352.23  -704.46          
## anthippretsimd1modelagesq     9 -689.77 -662.92 353.89  -707.77 3.3117  2
##                            Pr(>Chisq)
## anthippretsimd1modelagelin           
## anthippretsimd1modelagesq      0.1909
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                        Chisq Df Pr(>Chisq)    
## (Intercept)          28.2522  1  1.065e-07 ***
## RewardType            0.8867  1     0.3464    
## ageScaled             0.1883  1     0.6643    
## HighRewardStim        0.0311  1     0.8600    
## RewardType:ageScaled  0.5084  1     0.4758    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: similarity
##                        Chisq Df Pr(>Chisq)    
## (Intercept)          28.9388  1   7.47e-08 ***
## RewardType            0.8867  1     0.3464    
## ageScaled             0.0878  1     0.7669    
## HighRewardStim        0.0217  1     0.8828    
## goldretvoxnumScaled   0.8502  1     0.3565    
## RewardType:ageScaled  0.5084  1     0.4758    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Trialwise VTC ERS

Brain-Behavior & Brain-Brain Relations

Memory vs. Hippocampal ERS, VTC Encoding Similarity

  • AntHipp ERS absolute value = significant
  • VTC encoding similarity = marginal
## 
## Call:
## lm(formula = highvlowspecificmemd2 ~ highvlowERSabs + VTC.highvlowencsim + 
##     ageScaled + HighRewardStim, data = anthippRSAdatawbehavfullruns)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.284679 -0.062186  0.004287  0.051144  0.217183 
## 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                           -0.024624   0.024368  -1.010  0.31585   
## highvlowERSabs                         2.698326   0.788071   3.424  0.00105 **
## VTC.highvlowencsim                     0.841374   0.463675   1.815  0.07400 . 
## ageScaled                              0.001505   0.012110   0.124  0.90144   
## HighRewardStimHigh Reward Cat - Place -0.003522   0.024083  -0.146  0.88415   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1024 on 68 degrees of freedom
## Multiple R-squared:  0.2029, Adjusted R-squared:  0.156 
## F-statistic: 4.328 on 4 and 68 DF,  p-value: 0.003536

Hippocampal ERS vs. VTA Encoding Activation

  • VTA encoding activation = significant
  • age = marginal
## 
## Call:
## lm(formula = highvlowERS ~ VTA * ageScaled + HighRewardStim, 
##     data = anthippRSAdatawbehavfullruns)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.072556 -0.017396 -0.000258  0.019100  0.058890 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                           -0.0007279  0.0049501  -0.147   0.8835  
## VTA                                   -0.0216526  0.0095308  -2.272   0.0263 *
## ageScaled                              0.0073882  0.0040890   1.807   0.0752 .
## HighRewardStimHigh Reward Cat - Place  0.0021026  0.0066552   0.316   0.7530  
## VTA:ageScaled                          0.0068634  0.0112692   0.609   0.5445  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02708 on 68 degrees of freedom
## Multiple R-squared:  0.1335, Adjusted R-squared:  0.08252 
## F-statistic: 2.619 on 4 and 68 DF,  p-value: 0.04243

VTC Encoding Similarity vs. dlPFC Encoding Activation

  • dlPFC encoding activation = significant
## 
## Call:
## lm(formula = VTC.highvlowencsim ~ dlPFC * ageScaled + HighRewardStim, 
##     data = anthippRSAdatawbehavfullruns)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.067777 -0.016758 -0.002033  0.012705  0.058763 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)                            0.0031037  0.0042450   0.731    0.467
## dlPFC                                  0.0116198  0.0026357   4.409 3.79e-05
## ageScaled                             -0.0030698  0.0033570  -0.914    0.364
## HighRewardStimHigh Reward Cat - Place  0.0016807  0.0056392   0.298    0.767
## dlPFC:ageScaled                       -0.0006188  0.0024077  -0.257    0.798
##                                          
## (Intercept)                              
## dlPFC                                 ***
## ageScaled                                
## HighRewardStimHigh Reward Cat - Place    
## dlPFC:ageScaled                          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02388 on 68 degrees of freedom
## Multiple R-squared:  0.2265, Adjusted R-squared:  0.181 
## F-statistic: 4.977 on 4 and 68 DF,  p-value: 0.001405

Encoding Activation Tradeoff

  • significant
## 
## Call:
## lm(formula = dlPFC ~ VTA, data = anthippRSAdatawbehavfullruns)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2832 -0.6385 -0.1263  0.5527  3.7305 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.5456     0.1348   4.046 0.000131 ***
## VTA           0.8072     0.3467   2.328 0.022754 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.062 on 71 degrees of freedom
## Multiple R-squared:  0.07093,    Adjusted R-squared:  0.05785 
## F-statistic: 5.421 on 1 and 71 DF,  p-value: 0.02275